March 2024 arXiv papers — page 85

Daniel Tanneberg, Felix Ocker, Stephan Hasler, Joerg Deigmoeller

How can a robot provide unobtrusive physical support within a group of humans? We present Attentive Support, a novel interaction concept for robots to support a group of humans. It combines scene perception, dialogue acquisition, situation understanding, and behavior generation with the common-sense reasoning capabilities of Large Language Models (LLMs). In

Yuanhuiyi Lyu, Xu Zheng, Jiazhou Zhou, Lin Wang

We present UniBind, a flexible and efficient approach that learns a unified representation space for seven diverse modalities -- images, text, audio, point cloud, thermal, video, and event data. Existing works, eg., ImageBind, treat the image as the central modality and build an image-centered representation space; however, the space may be sub-optimal as it

Xinhui Rong, Victor Solo

Point processes are finding growing applications in numerous fields, such as neuroscience, high frequency finance and social media. So classic problems of classification and clustering are of increasing interest. However, analytic study of misclassification error probability in multi-class classification has barely begun. In this paper, we tackle the multi-c

Haruya Ishikawa, Takumi Iida, Yoshinori Konishi, Yoshimitsu Aoki

Generating annotations for bird's-eye-view (BEV) segmentation presents significant challenges due to the scenes' complexity and the high manual annotation cost. In this work, we address these challenges by leveraging the abundance of unlabeled data available. We propose the Perspective Cue Training (PCT) framework, a novel training framework that utilizes ps

Brian Godwin Lim, Galvin Brice Sy Lim, Renzo Roel Tan, Kazushi Ikeda

Graph neural networks (GNNs) have gained significant attention in recent years for their ability to process data that may be represented as graphs. This has prompted several studies to explore their representational capability based on the graph isomorphism task. Notably, these works inherently assume a countable node feature representation, potentially limi

Karel Dekimpe, Daciberg Lima Gonçalves, Oscar Ocampo

Let $n\geq 2$. Let $VB_n$ (resp. $VP_n$) denote the virtual braid group (resp. virtual pure braid group), let $WB_n$ (resp. $WP_n$) denote the welded braid group (resp. welded pure braid group) and let $UVB_n$ (resp. $UVP_n$) denote the unrestricted virtual braid group (resp. unrestricted virtual pure braid group). In the first part of this paper we prove th

Haibo Chen, Xiansheng Dai, Dong Liu, Yufeng Pei

In this paper, we introduce a family of functors denoted $\mathscr{F}_b$ that act on algebraic D-modules and generate modules over N=2 superconformal algebras. We prove these functors preserve irreducibility for all values of $b$, with a few clear exceptions described. We also establish necessary and sufficient conditions to determine when two such functors

Haochen Li, Di Geng

Events are essential components of speech and texts, describing the changes in the state of entities. The event extraction task aims to identify and classify events and find their participants according to event schemas. Manually predefined event schemas have limited coverage and are hard to migrate across domains. Therefore, the researchers propose Liberal

Sung-Soo Kim, Xiaobin Li, Satoshi Nawata, Futoshi Yagi

We report a novel BPS jumping phenomenon of 5d $\mathcal{N}=1$ supersymmetric gauge theories whose brane configuration is equipped with an O$7$-plane. The study of the relation between O$7{}^+$-plane and O$7{}^-$-plane reveals that such BPS jumps take place when the Higgsing is triggered near the O7-plane upon a particular parameter tuning of the theories. W

Chen Huang, Xiao Zhang, Yang Chen, Wen-Juan Zhong

Red dwarfs have been suggested to be among the possible astrophysical species accelerating particles and emitting TeV $\gamma$-rays. As an effort to search for the GeV $\gamma$-ray counterparts of the suggested TeV emission from eight red dwarfs, we analyse the 0.2--500 GeV $\gamma$-ray emission of the regions covering them exploiting the $\sim$13.6 yr Pass

Haochen Li, Di Geng

Events describe the state changes of entities. In a document, multiple events are connected by various relations (e.g., Coreference, Temporal, Causal, and Subevent). Therefore, obtaining the connections between events through Event-Event Relation Extraction (ERE) is critical to understand natural language. There are two main problems in the current ERE works

Bogdan Anastasiei, Nicoleta Dospinescu, Octavian Dospinescu

This research investigates the antecedents of positive and negative electronic word-of-mouth (eWOM) propensity, as well as the impact of eWOM propensity on the intention to repurchase the product. Two types of eWOM predictors were considered: product related variables and personal factors. The data were collected through an online survey conducted on a sampl

Pramit Rej

The purpose of this work is to investigate some interesting features of a static anisotropic relativistic stellar object composed of two different types of fluid distributions typically termed as quark matter (QM) and ordinary baryonic matter (OBM) together with Krori-Barua type (KB) {\em ansatz} in the regime of modified $f(\mathcal{G})$ gravity, where $\ma

Shijin Chen, Zeyi Liu, Xiao He, Dongliang Zou

The gearbox is a critical component of electromechanical systems. The occurrence of multiple faults can significantly impact system accuracy and service life. The vibration signal of the gearbox is an effective indicator of its operational status and fault information. However, gearboxes in real industrial settings often operate under variable working condit

FASER Collaboration, Roshan Mammen Abraham, John Anders, Claire Antel

This paper presents the first results of the study of high-energy electron and muon neutrino charged-current interactions in the FASER$\nu$ emulsion/tungsten detector of the FASER experiment at the LHC. A subset of the FASER$\nu$ volume, which corresponds to a target mass of 128.6~kg, was exposed to neutrinos from the LHC $pp$ collisions with a centre-of-mas

Lianyu Hu, Liqing Gao, Zekang Liu, Wei Feng

Skeleton-aware sign language recognition (SLR) has gained popularity due to its ability to remain unaffected by background information and its lower computational requirements. Current methods utilize spatial graph modules and temporal modules to capture spatial and temporal features, respectively. However, their spatial graph modules are typically built on

Eunkyung Park, Raymond K. Wong, Junbum Kwon

Artificial Intelligence has outperformed human experts in functional tasks such as chess and baduk. How about creative tasks? This paper evaluates AI's capability in the creative domain compared to human experts, which little research has been conducted so far. We propose a novel Prompt-for-Prompt to generate social media creatives via prompt augmentation by

Qi Zhang, Rui-Si Wang, Lei Wang

The behavior of polyatomic molecules around their equilibrium positions can be regarded as quantum coupled anharmonic oscillators. Solving the corresponding Schr\"odinger equations can interpret or predict experimental spectra of molecules. In this study, we develop a novel approach to solve excited states of anharmonic vibrational systems. The normal coordi

Pieter Belmans, Jishnu Bose, Sarah Frei, Benjamin Gould

We propose conjectural semiorthogonal decompositions for Fano schemes of linear subspaces on intersections of two quadrics, in terms of symmetric powers of the associated hyperelliptic (resp. stacky) curve. When the intersection is odd-dimensional, we moreover conjecture an identity in the Grothendieck ring of varieties and other motivic contexts. The eviden

Lejla Smajlović, Zenan Šabanac, Lamija Šćeta

In this paper we derive some new identities involving the Fibonacci and Lucas polynomials and the Chebyshev polynomials of the first and the second kind. Our starting point is a finite trigonometric sum which equals the resolvent kernel on the discrete circle with $m$ vertices and which can be evaluated in two different ways. An expression for this sum in te

Giancarlo Calvanese Strinati

The BCS-BEC crossover realized experimentally with ultra-cold Fermi gases may be considered as one of the important scientific achievements occurred during the last several years. The flexibility for operating on these systems on the experimental side and the full control of the relevant system degrees of freedom on the theoretical side make quite stringent

Ning Mao, Li-Chen Zhao

Most of previously reported dark-bright solitons admit identical width for the two components in both theoretical and experimental studies. We report dark-bright solitons can admit strikingly different widths, and derive a family of analytical solutions for them by Lagrangian variational method. The existence regimes for these solitons become much more wides

Juha Lehrbäck, Kaushik Mohanta, Antti V. Vähäkangas

We study non-local or fractional capacities in metric measure spaces. Our main goal is to clarify the relations between relative Hajlasz-Triebel-Lizorkin capacities, potentional Triebel-Lizorkin capacities, and metric space variants of Riesz capacities. As an application of our results, we obtain a characterization of a Hajlasz-Triebel-Lizorkin capacity dens

Uri Malamud, Morris Podolak, Joshua Podolak, Peter Bodenheimer

Uranus and Neptune are commonly considered ice giants, and it is often assumed that, in addition to a solar mix of hydrogen and helium, they contain roughly twice as much water as rock. This classical picture has led to successful models of their internal structure and has been understood to be compatible with the composition of the solar nebula during their

M. Rostami, S. S. Kia

This paper aims to enhance the use of the Frank-Wolfe (FW) algorithm for training deep neural networks. Similar to any gradient-based optimization algorithm, FW suffers from high computational and memory costs when computing gradients for DNNs. This paper introduces the application of the recently proposed projected forward gradient (Projected-FG) method to

Zhuowen Yuan, Zidi Xiong, Yi Zeng, Ning Yu

Recent advancements in Large Language Models (LLMs) have showcased remarkable capabilities across various tasks in different domains. However, the emergence of biases and the potential for generating harmful content in LLMs, particularly under malicious inputs, pose significant challenges. Current mitigation strategies, while effective, are not resilient und

Beomsu Kim, Jaemin Kim, Jeongsol Kim, Jong Chul Ye

Diffusion models (DMs) excel in unconditional generation, as well as on applications such as image editing and restoration. The success of DMs lies in the iterative nature of diffusion: diffusion breaks down the complex process of mapping noise to data into a sequence of simple denoising tasks. Moreover, we are able to exert fine-grained control over the gen

Mustafa Sencer Aydın, Pranava Chaitanya Jayanti

We address the long-time behavior of the 2D Boussinesq system, which consists of the incompressible Navier-Stokes equations driven by a non-diffusive density. We construct globally persistent solutions on a smooth bounded domain, when the initial data belongs to $(H^k\cap V)\times H^k$ for $k\in\mathbb{N}$ and $H^s\times H^s$ for $0<s<2$. The proofs use para

Amar Bharti, Gopal Dixit

To add to the rapidly progressing field of ultrafast photocurrent, we propose a universal method to generate photocurrent in normal and topological materials using a pair of multicycle linearly polarized laser pulses. The interplay of the fundamental and its second harmonic pulses is studied for the generation of photocurrent in Weyl semimetals by varying th

Anwesha Kanjilal, Ali Ahmadian, Martina Freund, Pei-Ling Sun

The influence of temperature on the deformation behaviour of the C15 CaAl$_2$ Laves phase, a key constituent for enhancing the mechanical properties of Mg alloys up to service temperatures of 200 {\deg}C, remains largely unexplored. This study presents, for the first time, the nanoscale brittle-to-ductile transition (BDT) of this intermetallic phase through

Xu Shi, Xuehan Wang, Jingbo Tan, Jintao Wang

Extremely large-scale antenna array (ELAA) is promising as one of the key ingredients for the sixth generation (6G) of wireless communications. The electromagnetic propagation of spherical wavefronts introduces an additional distance-dependent dimension beyond conventional beamspace. In this paper, we first present one concise closed-form channel formulation

Xu Zheng, Pengyuan Zhou, Athanasios V. Vasilakos, Lin Wang

This paper addresses an interesting yet challenging problem -- source-free unsupervised domain adaptation (SFUDA) for pinhole-to-panoramic semantic segmentation -- given only a pinhole image-trained model (i.e., source) and unlabeled panoramic images (i.e., target). Tackling this problem is nontrivial due to the semantic mismatches, style discrepancies, and

Chaoran Xiong, Guoqing Liu, Qi Wu, Songpengcheng Xia

Temporal misalignment (time offset) between sensors is common in low cost visual-inertial odometry (VIO) systems. Such temporal misalignment introduces inconsistent constraints for state estimation, leading to a significant positioning drift especially in high dynamic motion scenarios. In this article, we focus on online temporal calibration to reduce the po

Sara Abdali, Richard Anarfi, CJ Barberan, Jia He

Large language models (LLMs) have significantly transformed the landscape of Natural Language Processing (NLP). Their impact extends across a diverse spectrum of tasks, revolutionizing how we approach language understanding and generations. Nevertheless, alongside their remarkable utility, LLMs introduce critical security and risk considerations. These chall

Jihao Li, Tingbo Liao, Hassen Nigatu, Haotian Guo

Under-actuated robot grippers as a pervasive tool of robots have become a considerable research focus. Despite their simplicity of mechanical design and control strategy, they suffer from poor versatility and weak adaptability, making widespread applications limited. To better relieve relevant research gaps, we present a novel 3-finger linkage-based gripper

Juntao Yang

In this paper, we extend our work to the Bayesian inverse problems for inferring unknown forcing and initial condition of the forward Navier-Stokes equation coupled with tracer equation with noisy Lagrangian observation on the positions of the tracers. We consider the Navier-Stokes equations in the two dimensional periodic torus with a tracer equation which

Shiki Sato, Reina Akama, Jun Suzuki, Kentaro Inui

Mitigating the generation of contradictory responses poses a substantial challenge in dialogue response generation. The quality and quantity of available contradictory response data play a vital role in suppressing these contradictions, offering two significant benefits. First, having access to large contradiction data enables a comprehensive examination of

Yubao Tang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke

Recently, a novel generative retrieval (GR) paradigm has been proposed, where a single sequence-to-sequence model is learned to directly generate a list of relevant document identifiers (docids) given a query. Existing GR models commonly employ maximum likelihood estimation (MLE) for optimization: this involves maximizing the likelihood of a single relevant

Hyuckjin Choi, A. Lee Swindlehurst, Junil Choi

Reconfigurable intelligent surface (RIS) technology, given its ability to favorably modify wireless communication environments, will play a pivotal role in the evolution of future communication systems. This paper proposes rate maximization techniques for both single-user and multiuser MIMO systems, based on the well-known weighted minimum mean square error

Huanxin Chen, Chun Xia, Hechao Chen

To understand the magnetic fields of the polar crown filaments (PCFs) at high latitudes near polar regions of the Sun, we perform magnetofrictional numerical simulations on the long-term magnetic evolution of bipolar fields with roughly east-west polarity inversion lines (PILs) in a three-dimensional (3D) spherical wedge domain near polar regions. The Coriol

Yin-Ting Zhang, Mu-Yao Li, Wei-Xing Zhou

The Russia-Ukraine conflict is a growing concern worldwide and poses serious threats to regional and global food security. Using monthly trade data for maize, rice, and wheat from 2016/1 to 2022/12, this paper constructs three international crop trade networks (iCTNs) and an aggregate international food trade network (iFTN). We aim to examine the structural

Zhipeng Li, Nick Birbilis

The design and discovery of new materials is fundamental to advancing scientific and technological innovation. The recent emergence of the materials genome concept holds great promise in revolutionising materials science by enabling the systematic utilisation of data for efficient prediction and optimisation of superior materials. However, the materials geno

Pengfei Zhu, Yang Sun, Bing Cao, Qinghua Hu

General image fusion aims at integrating important information from multi-source images. However, due to the significant cross-task gap, the respective fusion mechanism varies considerably in practice, resulting in limited performance across subtasks. To handle this problem, we propose a novel task-customized mixture of adapters (TC-MoA) for general image fu

Wolfgang Fuhl

Scanpath classification is an area in eye tracking research with possible applications in medicine, manufacturing as well as training systems for students in various domains. In this paper we propose a trainable feature extraction module for deep neural networks. The purpose of this module is to transform a scanpath into a feature vector which is directly us

Vladimir Tarkaev

We prove a prime decomposition theorem for string links in a thickened surface. Namely, we prove that any non-braid string link $\ell \subset \Sigma \times I$, where $\Sigma$ is a compact orientable (not necessarily closed) surface other than $S^2$, can be written in the form $\ell =\ell_1 \# \ldots \# \ell_m$, where $\ell_j,j=1,\ldots,m,$ is prime string li

Bilol Banerjee, Anil K. Ghosh

We develop a test for spherical symmetry of a multivariate distribution $\Pr$ that works well even when the dimension of the data $d$ is larger than the sample size $n$. We propose a non-negative measure of spherical asymmetry $\zeta(\Pr)$ such that $\zeta(\Pr)=0$ if and only if $\Pr$ is spherically symmetric. We construct a consistent estimator of $\zeta(\P

Bora Çarpınlıoğlu, Uğur Teğin

Today, machine learning tools, particularly artificial neural networks, have become crucial for diverse applications. However, current digital computing tools to train and deploy artificial neural networks often struggle with massive data sizes and high power consumptions. Optical computing provides inherent parallelism accommodating high-resolution input da

Hanspeter Kraft, Mikhail Zaidenberg

This is a brief overview of a few selected chapters on automorphism groups of affine varieties. It includes some open questions.

Yixuan Wu, Yizhou Wang, Shixiang Tang, Wenhao Wu

We present DetToolChain, a novel prompting paradigm, to unleash the zero-shot object detection ability of multimodal large language models (MLLMs), such as GPT-4V and Gemini. Our approach consists of a detection prompting toolkit inspired by high-precision detection priors and a new Chain-of-Thought to implement these prompts. Specifically, the prompts in th

Ao Lu, Runfeng Li, Yunchang Yu, Ziwang Lu

The four-wheel independent drive and four-wheel independent steering (4WID-4WIS) configurations enhance control flexibility and dynamic performance potential for more integrated electric vehicles. This paper comprehensively analyzes the impacts of four key factors on tire force control allocation: vertical load estimation, actuator dynamic characteristics, t

Chengxi Li, Mikael Skoglund

This paper considers the problem of distributed learning (DL) in the presence of stragglers. For this problem, DL methods based on gradient coding have been widely investigated, which redundantly distribute the training data to the workers to guarantee convergence when some workers are stragglers. However, these methods require the workers to transmit real-v

Guoxuan Xia, Olivier Laurent, Gianni Franchi, Christos-Savvas Bouganis

Label smoothing (LS) is a popular regularisation method for training neural networks as it is effective in improving test accuracy and is simple to implement. ``Hard'' one-hot labels are ``smoothed'' by uniformly distributing probability mass to other classes, reducing overfitting. Prior work has suggested that in some cases LS can degrade selective classifi

Jingren Liu, Zhong Ji, Yanwei Pang, YunLong Yu

The proliferation of Few-Shot Class Incremental Learning (FSCIL) methodologies has highlighted the critical challenge of maintaining robust anti-amnesia capabilities in FSCIL learners. In this paper, we present a novel conceptualization of anti-amnesia in terms of mathematical generalization, leveraging the Neural Tangent Kernel (NTK) perspective. Our method

Xiao-Gang He, Xiao-Dong Ma, Michael A. Schmidt, German Valencia

Recently Belle II reported the first measurement of $B^+\to K^++{\rm invisible (inv)}$, which is $2.7\sigma$ above the standard model (SM) prediction. If confirmed, this calls for new physics beyond SM. In the SM, the invisible particles are neutrino-anti-neutrino pairs. There are more possibilities when going beyond the SM. In this work, we focus on decays

Shamim Sk, Naoki Sato, Takao Mori

Here, we investigate the thermoelectric properties of the marcasite-type compounds MSb$_2$ (M = Ta, Nb) in the temperature range of 310-730 K. These compounds were synthesized by a solid-state reaction followed by the spark plasma sintering process. The Rietveld refinement method confirms the monoclinic phase with space group C2/m for both compounds. The obs

Aakash Singh, Vivek Kumar Singh

The advancements in computer vision and image processing techniques have led to emergence of new application in the domain of visual surveillance, targeted advertisement, content-based searching, and human-computer interaction etc. Out of the various techniques in computer vision, face analysis, in particular, has gained much attention. Several previous stud

Xudong Guo, Kaixuan Huang, Jiale Liu, Wenhui Fan

Large Language Models (LLMs) have emerged as integral tools for reasoning, planning, and decision-making, drawing upon their extensive world knowledge and proficiency in language-related tasks. LLMs thus hold tremendous potential for natural language interaction within multi-agent systems to foster cooperation. However, LLM agents tend to over-report and com

Seil Kang, Donghyun Kim, Junhyeok Kim, Hyo Kyung Lee

Significant methodological strides have been made toward Chest X-ray (CXR) understanding via modern vision-language models (VLMs), demonstrating impressive Visual Question Answering (VQA) and CXR report generation abilities. However, existing CXR understanding frameworks still possess several procedural caveats. (1) Previous methods solely use CXR reports, w

Eunjee Choi, Jong-Kook Kim

Detecting fake news has received a lot of attention. Many previous methods concatenate independently encoded unimodal data, ignoring the benefits of integrated multimodal information. Also, the absence of specialized feature extraction for text and images further limits these methods. This paper introduces an end-to-end model called TT-BLIP that applies the

Di Zhou

Particle-hole symmetry and chiral symmetry play a pivotal role in multiple areas of physics, yet they remain unstudied in systems with nonlinear interactions whose nonlinear normal modes do not exhibit $\textbf{U}(1)$-gauge symmetry. In this work, we establish particle-hole symmetry and chiral symmetry in such systems. Chiral symmetry ensures the quantizatio

Matthew Randall

We establish a correspondence between solutions of Noth's equation, a non-linear ordinary differential equation that shows up in the theory of $(2,3,5)$-distributions, and diffeomorphisms of any contact structure of type $G_{2}$ to the standard one.

Cai-Yuan Ye, Hong-Ming Weng, Quan-Sheng Wu

In recent years, progress has been made in generating new crystalline materials using generative machine learning models, though gaps remain in efficiently generating crystals based on target properties. This paper proposes the Con-CDVAE model, an extension of the Crystal Diffusion Variational Autoencoder (CDVAE), for conditional crystal generation. We intro

Yuto Ishikawa, Kohei Konaka, Tomohiko Nakamura, Norihiro Takamune

Real-time speech extraction is an important challenge with various applications such as speech recognition in a human-like avatar/robot. In this paper, we propose the real-time extension of a speech extraction method based on independent low-rank matrix analysis (ILRMA) and rank-constrained spatial covariance matrix estimation (RCSCME). The RCSCME-based meth

Sungmun Cho, Taeyeoup Kang

In this paper we describe each coefficient of the Siegel series associated to a quadratic $\mathfrak{o}$-lattice $L$ in terms of lattice counting problems, where $\mathfrak{o}$ is the ring of integers of a non-Archimedean local field of characteristic $0$. Under the restriction that $p$ is odd and that the dimension of the radical of the quadratic space $L\o

Yu-Hao Shen, Jun-Ding Zheng, Wen-Yi Tong, Zhi-Qiang Bao

Twisted van der Waals bilayers offer ideal two-dimensional (2D) platforms for exploring the intricate interplay between the spin and charge degrees of freedom of electrons. By investigating twisted MoS2 bilayer, featuring two distinct stackings but with identical commensurate supercell sizes, we reveal an unusual dielectric response behavior inherent to this

Cheng Yang, Jixi Liu, Yunhe Yan, Chuan Shi

Despite the remarkable success of graph neural networks (GNNs) in modeling graph-structured data, like other machine learning models, GNNs are also susceptible to making biased predictions based on sensitive attributes, such as race and gender. For fairness consideration, recent state-of-the-art (SOTA) methods propose to filter out sensitive information from

Manana Kachakhidze, Nino Kachakhidze-Murphy, Giorgi Ramishvili, Badri Khvitia

The article discusses the possibilities of three-step early warning and short-term prediction of earthquakes based on the classical geological model of fault formation and a model of the generation of electromagnetic emissions detected before earthquakes. These possibilities are revealed by the analysis of frequency data of VLF/LF electromagnetic radiation,

Wendi Yang, Zihang Jiang, Shang Zhao, S. Kevin Zhou

With the recent advancements in single-image-based human mesh recovery, there is a growing interest in enhancing its performance in certain extreme scenarios, such as occlusion, while maintaining overall model accuracy. Although obtaining accurately annotated 3D human poses under occlusion is challenging, there is still a wealth of rich and precise 2D pose a

Alexey Kokotov, Dmitrii Korikov

Let $P$ be a point of a compact Riemann surface $X$. We study self-adjoint extensions of the Dolbeault Laplacians in hermitian line bundles $L$ over $X$ initially defined on sections with compact supports in $X\backslash\{P\}$. We define the $\zeta$-regularized determinants for these operators and derive comparison formulas for them. We introduce the notion

Keqi Zhu, Haotian Guo, Wei Yu, Hassen Nigatu

Recent research on mobile robots has focused on increasing their adaptability to unpredictable and unstructured environments using soft materials and structures. However, the determination of key design parameters and control over these compliant robots are predominantly iterated through experiments, lacking a solid theoretical foundation. To improve their e

Simon Schaefer, Juan D. Galvis, Xingxing Zuo, Stefan Leutengger

We present a novel 3D shape completion framework that unifies multimodal conditioning, leveraging both 2D images and 3D partial scans through a latent diffusion model. Shapes are represented as Truncated Signed Distance Functions (TSDFs) and encoded into a discrete latent space jointly supervised by 2D and 3D cues, enabling efficient high-resolution processi

Ojas Nimase, Sanghyun Hong

Sarcasm recognition is challenging because it needs an understanding of the true intention, which is opposite to or different from the literal meaning of the words. Prior work has addressed this challenge by developing a series of methods that provide richer $contexts$, e.g., sentiment or cultural nuances, to models. While shown to be effective individually,

Danqing Luo, Chen Zhang, Yan Zhang, Haizhou Li

Training or finetuning large-scale language models (LLMs) requires substantial computation resources, motivating recent efforts to explore parameter-efficient adaptation to downstream tasks. One approach is to treat these models as black boxes and use forward passes (Inference APIs) to interact with them. Current research focuses on adapting these black-box

Heng Wang, Jianhua Zhang, Gaofeng Nie, Li Yu

Digital twin channel (DTC) is the real-time mapping of a wireless channel from the physical world to the digital world, which is expected to provide significant performance enhancements for the sixth-generation (6G) air-interface design. In this work, we first define five evolution levels of channel twins with the progression of wireless communication. The f

Yunhan Ren, Bo Li, Chengyang Zhang, Yong Zhang

Existing object localization methods are tailored to locate specific classes of objects, relying heavily on abundant labeled data for model optimization. However, acquiring large amounts of labeled data is challenging in many real-world scenarios, significantly limiting the broader application of localization models. To bridge this research gap, this paper d

Qilin Sun, Weiming Zhi, Tianyi Zhang, Matthew Johnson-Roberson

This paper introduces Spatial Diagrammatic Instructions (SDIs), an approach for human operators to specify objectives and constraints that are related to spatial regions in the working environment. Human operators are enabled to sketch out regions directly on camera images that correspond to the objectives and constraints. These sketches are projected to 3D

Eduardo L. Martín, Jerry J. -Y. Zhang, Honorio Lanchas, Nicolas Lodieu

Observations of five Y dwarfs with three optical and near-infrared instruments at the 10.4 m Gran Telescopio Canarias are reported. Deep images of the five targets and a low-resolution far-red optical spectrum for one of the targets were obtained. One of the Y dwarfs, WISE J173835+273258 (Y0), was clearly detected in the optical (z- and i-bands) and another,

Morgane Joly, Fabian Rivière, Éric Renault

This paper describes a receiver that uses an innovative method to predict, according to history of receiver operating metrics (packet lost/well received), the optimum automatic gain control (AGC) index or most appropriate variable gain range to be used for next packet reception, anticipating an interferer appearing during the payload reception. This allows t

Biswadeep Chakraborty, Saibal Mukhopadhyay

Spiking Neural Networks (SNNs) have become an essential paradigm in neuroscience and artificial intelligence, providing brain-inspired computation. Recent advances in literature have studied the network representations of deep neural networks. However, there has been little work that studies representations learned by SNNs, especially using unsupervised loca

Manash Dey, Subhankar Roy

Amid the uncertainty regarding the fundamental nature of neutrinos, we adhere to the Dirac description, and construct a model in the framework of $\Delta(27)$ symmetry. The model successfully accounts for the hierarchical patterns of both charged lepton and neutrino masses. The neutrino mass matrix exhibits four texture zeroes, and the associated mixing sche

Qinian Jin, Liuhong Chen

In this paper we apply the stochastic variance reduced gradient (SVRG) method, which is a popular variance reduction method in optimization for accelerating the stochastic gradient method, to solve large scale linear ill-posed systems in Hilbert spaces. Under {\it a priori} choices of stopping indices, we derive a convergence rate result when the sought solu

Yifei Wang, Qi Zhang, Yaoyu Guo, Yisen Wang

Deep representations have shown promising performance when transferred to downstream tasks in a black-box manner. Yet, their inherent lack of interpretability remains a significant challenge, as these features are often opaque to human understanding. In this paper, we propose Non-negative Contrastive Learning (NCL), a renaissance of Non-negative Matrix Facto

Liana M. Şega, Deepak Sireeshan

Let $(Q,\mathfrak{m},{\sf k})$ be a local ring that admits an exact pair of zero divisors $(f,g)$, $M$ a $Q$-module with $fM = 0$ and $U$ a free resolution of $M$ over $Q$. We construct a degree $-2$ chain map, which we call an Einsenbud operator, on the complex $U \otimes_Q Q/(f,g)$ and use the mapping cone of the operator to study two exact sequences that

Yuxi Mi, Zhizhou Zhong, Yuge Huang, Jiazhen Ji

The widespread adoption of face recognition has led to increasing privacy concerns, as unauthorized access to face images can expose sensitive personal information. This paper explores face image protection against viewing and recovery attacks. Inspired by image compression, we propose creating a visually uninformative face image through feature subtraction

Yunyun Wang, Tatsushi Oka, Dan Zhu

Inflation exhibits state-dependent, skewed, and fat-tailed dynamics that make risk a central concern for monetary policy. Accordingly, inflation risks are distributional and cannot be fully captured by mean-based models. We propose a flexible time-varying parameter distributional regression model that estimates the full conditional distribution of inflation,

Wenqi Zhu, Jiale Cao, Jin Xie, Shuangming Yang

Open-vocabulary video instance segmentation strives to segment and track instances belonging to an open set of categories in a videos. The vision-language model Contrastive Language-Image Pre-training (CLIP) has shown robust zero-shot classification ability in image-level open-vocabulary tasks. In this paper, we propose a simple encoder-decoder network, call

Soumendra Kumar Das, Lokanath Patra, Prasanjit Samal, Pratap Kumar Sahoo

The process of environmental oxidation is pivotal in determining the physical and chemical properties of two-dimensional (2D) materials. Its impact holds great significance for the practical application of these materials in nanoscale devices functioning under ambient conditions. This study delves into the influence of O2 and O3 exposure on the structural an

Zhangjie Peng, Zhaotian Li, Ruijing Liu, Cunhua Pan

In this paper, we consider an reconfigurable intelligent surface (RIS)-aided frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) downlink system.In the FDD systems, the downlink channel state information (CSI) should be sent to the base station through the feedback link. However, the overhead of CSI feedback occupies substantial upl

Lirui Luo, Guoxi Zhang, Hongming Xu, Yaodong Yang

Neuro-symbolic reinforcement learning (NS-RL) has emerged as a promising paradigm for explainable decision-making, characterized by the interpretability of symbolic policies. NS-RL entails structured state representations for tasks with visual observations, but previous methods cannot refine the structured states with rewards due to a lack of efficiency. Acc

Zongnan Ma, Fuchun Zhang, Zhixiong Nan, Yao Ge

Anticipating human intention from videos has broad applications, such as automatic driving, robot assistive technology, and virtual reality. This study addresses the problem of intention action anticipation using egocentric video sequences to estimate actions that indicate human intention. We propose a Hierarchical Complete-Recent (HCR) information fusion mo

Seunghyeon Lim, Youngjae Yoo, Jun Ki Lee, Byoung-Tak Zhang

In this paper, we propose a novel method for plane clustering specialized in cluttered scenes using an RGB-D camera and validate its effectiveness through robot grasping experiments. Unlike existing methods, which focus on large-scale indoor structures, our approach -- Multi-Object RANSAC emphasizes cluttered environments that contain a wide range of objects

Yifei Wang, Jizhe Zhang, Yisen Wang

Contrastive Learning (CL) has emerged as one of the most successful paradigms for unsupervised visual representation learning, yet it often depends on intensive manual data augmentations. With the rise of generative models, especially diffusion models, the ability to generate realistic images close to the real data distribution has been well recognized. Thes

Omar Mustafa

We investigate the bosonic Klein-Gordon (KG) oscillators in a global monopole (GM) spacetime in Eddington-inspired Born-Infeld (EiBI) gravity and a Wu-Yang magnetic monopole (WYMM). We discuss the gravitational effects in the presence of Ricci scalar curvature $R=R_{\upsilon }^{\upsilon }$. It is observed that the presence of the Ricci scalar curvature, effe

Xinyu Zhang, Siddharth Muralee, Sourag Cherupattamoolayil, Aravind Machiry

GitHub workflows or GitHub CI is a popular continuous integration platform that enables developers to automate various software engineering tasks by specifying them as workflows, i.e., YAML files with a list of jobs. However, engineering valid workflows is tedious. They are also prone to severe security issues, which can result in supply chain vulnerabilitie

Sensen Gao, Xiaojun Jia, Xuhong Ren, Ivor Tsang

Vision-language pre-training (VLP) models exhibit remarkable capabilities in comprehending both images and text, yet they remain susceptible to multimodal adversarial examples (AEs). Strengthening attacks and uncovering vulnerabilities, especially common issues in VLP models (e.g., high transferable AEs), can advance reliable and practical VLP models. A rece

Xiaoben Li, Mancheng Meng, Ziyan Wu, Terrence Chen

Multi-view 3D human pose estimation is naturally superior to single view one, benefiting from more comprehensive information provided by images of multiple views. The information includes camera poses, 2D/3D human poses, and 3D geometry. However, the accurate annotation of these information is hard to obtain, making it challenging to predict accurate 3D huma

Saurabh Kumar, Akhil Uniyal, Sayan Chakrabarti

We have investigated the characteristics of shadows cast by the Kerr black hole in the presence of plasma and compared them to those of a rotating wormhole in a uniform plasma space-time for an observer at infinity. Interestingly, for the same uniform plasma density, the apparent shadow size of the rotating wormhole is always greater than that of the Kerr bl

Qingshan Xu, Jiao Liu, Melvin Wong, Caishun Chen

Text-to-3D generation has shown great promise in generating novel 3D content based on given text prompts. However, existing generative methods mostly focus on geometric or visual plausibility while ignoring precise physics perception for the generated 3D shapes. This greatly hinders the practicality of generated 3D shapes in real-world applications. In this

Hangdong Zhao, Shaleen Deep, Paraschos Koutris, Sudeepa Roy

Datalog is a powerful yet elegant language that allows expressing recursive computation. Although Datalog evaluation has been extensively studied in the literature, so far, only loose upper bounds are known on how fast a Datalog program can be evaluated. In this work, we ask the following question: given a Datalog program over a naturally-ordered semiring $\

Hai-Yang Mu, Zhou Fan, Yi-Nan Zhu, Yu Zhang

This work shows details of an evaluation of an observational system comprising a CMOS detector, 60-cm telescope, and filter complement. The system's photometric precision and differential photometric precision, and extinction coefficients were assessed through observations of Supersky flat fields, open clusters, standard stars, and exoplanets. Photometry was